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Agile Machine Learning with DataRobot

You're reading from   Agile Machine Learning with DataRobot Automate each step of the machine learning life cycle, from understanding problems to delivering value

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801076807
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Bipin Chadha Bipin Chadha
Author Profile Icon Bipin Chadha
Bipin Chadha
Sylvester Juwe Sylvester Juwe
Author Profile Icon Sylvester Juwe
Sylvester Juwe
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Foundations
2. Chapter 1: What Is DataRobot and Why You Need It? FREE CHAPTER 3. Chapter 2: Machine Learning Basics 4. Chapter 3: Understanding and Defining Business Problems 5. Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6. Chapter 4: Preparing Data for DataRobot 7. Chapter 5: Exploratory Data Analysis with DataRobot 8. Chapter 6: Model Building with DataRobot 9. Chapter 7: Model Understanding and Explainability 10. Chapter 8: Model Scoring and Deployment 11. Section 3: Advanced Topics
12. Chapter 9: Forecasting and Time Series Modeling 13. Chapter 10: Recommender Systems 14. Chapter 11: Working with Geospatial Data, NLP, and Image Processing 15. Chapter 12: DataRobot Python API 16. Chapter 13: Model Governance and MLOps 17. Chapter 14: Conclusion 18. Other Books You May Enjoy

Chapter 3: Understanding and Defining Business Problems

This chapter covers topics that are the most critical for success and yet are not discussed in detail in data science programs or books. Although the topic of understanding and defining business problems is mentioned very briefly as something that should be done, it is very rare that the discussion will go into how to actually do it properly. In this chapter, we will go into specific tools and methods that can be used to gain an understanding of the system under consideration and determine the problem that needs to be solved.

This section is independent of DataRobot, as DataRobot cannot help you with this part of the process. This is something that a data analyst, a business analyst, or a data scientist has to do. Correctly defining a business problem is hard to do—it is not automatable, and it is also not done properly most of the time. If you gain this skill, you will become invaluable. This is a key area where there...

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